Cardiovascular disease is the leading cause of death in the United States with many deaths attributable to preventive causes. Congestive heart failure (CHF), as an emerging epidemic with significant burden on hospitalizations, quality of life, and societal cost, warrants special attention. It has experienced little improvement in hospital admissions in the past three decades and is a leading cause of death in the United States with approximately 670,000 individuals diagnosed every year. It is also an end-stage condition reached by many with other cardiovascular diseases, such as diabetes, hypertension, and atherosclerosis, all of which are increasing in prevalence at alarming rates in the United States.
The clinical course of CHF has also been well documented. Given the known inciting events preceding hospitalization, the lack of methods to accurately predict these changes, and frequency of decompensation resulting in recurrent hospitalizations, experts believe that constant monitoring of patients with CHF is essential to a patient's health.
Remote sensing has thus emerged as a twofold solution to the unsustainable trends in treating heart failure by also expanding healthcare access and clinical surveillance to the growing demographics that are at-risk, limited in healthcare access, or both. While various medical specialties have attempted to implement remote monitoring solutions with varying levels of success, at least some methods for longitudinal monitoring are limited or unreliable in detecting emergent conditions as reflected in significant rehospitalization rates and resultant economic burden, such as from ambulance costs and emergency room visits. At least some of these monitoring methods may suffer from a paucity of physiological sensed quantities, obtrusive sensing requirements, noise or artifact corruption, or poor classification algorithms.
In the field of cardiology, many sensors may report information with heavy dependence on a measure such as body weight, EKG, or transthoracic impedance with limited potential for evaluating more integrated phenomena, such as heart failure status. Though some recent efforts have shown potential for early prediction of emergent conditions by including addition sensors, these approaches have not yet fully applied analytical and statistical tools for an automated biocomputational approach to disease modeling and intervention with acceptable performance or clinical adoption, as in the remote monitoring of acute decompensated heart failure (ADHF). Heart failure is especially suited to remote monitoring because of its inordinate toll on society, the indolent progression of disease, and the ineffective treatment methods currently available for those suffering recent myocardial infarction or other cardiac insult.
For effective longitudinal monitoring, the classification and disease progression computation must maximize acquisition of ideal data points, hence requiring patient compliance over a long time course, such as six or twelve months. Inconvenient or uncomfortable device placement or weight, limitations on physical activity, prohibitive costs, and supervised data transmission or general use are examples of barriers to long term patient compliance. Preferred embodiments of the present invention allow for continuous or substantially continuous monitoring with an external and cost-efficient adherent patch. The patch may use existing cellular infrastructure to disseminate processing and decrease computation load in confined spaces to realize power and memory savings and provide a more comfortable device with smaller battery and memory requirements, allowing for a more economical sensor able to benefit more individuals with remote monitoring to track health or disease status and predict emergent conditions, such as ADHF.
The presented invention addresses this need for improved remote monitoring of physiological measures implicated in chronic pathologies by using a classification algorithm to monitor individuals and alert caregivers to health and disease state and probability of future emergent conditions. This method is enabled by disseminated processing to allow for comprehensive health modeling, improved patient compliance, and economic feasibility of the multisensor device. While an embodiment of this invention can be applied to monitoring general health or chronic pathologies such as heart failure, many other conditions can be monitored, including but in no way limited to, diabetes, obesity, depression, epilepsy, respiratory diseases, or hypertension, independent of etiology.